The overall objective of the present disclosure is to enhance and automate several aspects of client prospect identification, discovery, and analysis using a combination of machine learning, natural language processing, mathematical modeling, and relational database modeling. By doing so, prospect results are provided more efficiently and at a higher quality. Prospect identification, for example, may aid in accurately generating prospects that fit a broker's interest in insurance products and industries (i.e., the broker's “appetite”). Brokers typically have a current list of clients that defines the broker's appetite. Brokers typically want to find other similar companies to the existing clients.
When prospecting, a broker typically strives to identify other similar brokers who may operate in a target area or who may be connected to a target prospect in some capacity. Due to the scale of large brokerage companies, it is impossible for a broker to have all this information at hand. However, by leveraging electronic communications data, one can build a profile of connections between brokers, insurance companies, and clients, via brokers' email interactions.
Electronic communications, such as emails, text messages, and intra-application messaging tools (e.g., Facebook messenger) allow individuals to collaborate, negotiate, and debate issues while creating a virtual “audit trail” of discourse. Businesses worldwide depend more and more upon these methods of communications, both inside an organization and when communicating with customers, clients, and other external collaborators.
Relationship maintenance, anticipation of needs of business partners (e.g., customer, client, joint venturer, project collaborator, fellow board member, other inter-office client-like relationships), and effective communications are essential for businesses to thrive. While some cycles are well-known (e.g., consumer purchase cycles and the holidays, etc.), cycles of business needs of particular business partners or within particular business partner demographic classifications (e.g., geographies, sectors, industries, etc.) may be more subtle.
The inventors recognized an untapped advantage in using historic electronic communications to develop insights into relationships, patterns or cycles of business partner needs, and correlations between customer/client communications and deal (e.g., negotiation, client retention, etc.) outcomes. In an illustrative example, a company with 7000 brokers, each sending or receiving 100 emails per day, will generate over 250 million emails per year. Due to the scale of large brokerage companies, by leveraging email data, one can build a profile of connections between brokers, insurance companies, and clients, via brokers' email interactions. By developing tools to analyze this email data, new discoveries can be made, including identifying key relationships and under-reported marketing behavior.